Computer Science > Machine Learning
[Submitted on 30 Mar 2023 (v1), last revised 16 Dec 2023 (this version, v3)]
Title:FairGen: Towards Fair Graph Generation
View PDF HTML (experimental)Abstract:There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite the remarkable success, the vast majority of these works are unsupervised in nature and are typically trained to minimize the expected graph reconstruction loss, which would result in the representation disparity issue in the generated graphs, i.e., the protected groups (often minorities) contribute less to the objective and thus suffer from systematically higher errors. In this paper, we aim to tailor graph generation to downstream mining tasks by leveraging label information and user-preferred parity constraints. In particular, we start from the investigation of representation disparity in the context of graph generative models. To mitigate the disparity, we propose a fairness-aware graph generative model named FairGen. Our model jointly trains a label-informed graph generation module and a fair representation learning module by progressively learning the behaviors of the protected and unprotected groups, from the `easy' concepts to the `hard' ones. In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability. Experimental results on seven real-world data sets, including web-based graphs, demonstrate that FairGen (1) obtains performance on par with state-of-the-art graph generative models across nine network properties, (2) mitigates the representation disparity issues in the generated graphs, and (3) substantially boosts the model performance by up to 17% in downstream tasks via data augmentation.
Submission history
From: Lecheng Zheng [view email][v1] Thu, 30 Mar 2023 23:30:42 UTC (2,907 KB)
[v2] Tue, 25 Jul 2023 01:50:39 UTC (2,908 KB)
[v3] Sat, 16 Dec 2023 22:42:42 UTC (4,038 KB)
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